{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f04589a37b99ed5446182256aa875a9373b8a295"
   },
   "source": [
    "# Introduction\n",
    "\n",
    "The data comes from the past Kaggle challenge \"Challenges in Representation Learning: Facial Expression Recognition Challenge\":\n",
    "\n",
    "https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\n",
    "\n",
    "The data consists of 48x48 pixel grayscale images of faces. The faces have been automatically registered so that the face is more or less centered and occupies about the same amount of space in each image. Each image corresponds to a facial expression in one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). The dataset contains approximately 36K images.\n",
    "\n",
    "The original data consisted in arrays with a greyscale value for each pixel. We converted this data into raw images and splitted them in multiple folders: \n",
    "\n",
    "images/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;train/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;angry/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;disgust/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fear/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;happy/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;neutral/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sad/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;surprise/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;validation/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;angry/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;disgust/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fear/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;happy/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;neutral/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sad/<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;surprise/<br>\n",
    "\n",
    "80% of our images are contained inside the train folder, and the last 20% are inside the validation folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "050307096c25e11ef3fd75df86a9477903882246"
   },
   "source": [
    "# Quick data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b2676e8f24dcdede793d3efd7cfb84f6c624f695"
   },
   "source": [
    "First let's see how our images look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_uuid": "ada86668a25cba36e2676a20bc76f96581057d26"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1440 with 35 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display some images for every different expression\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# size of the image: 48*48 pixels\n",
    "pic_size = 48\n",
    "\n",
    "# input path for the images\n",
    "base_path = \"../input/images/images/\"\n",
    "\n",
    "plt.figure(0, figsize=(12,20))\n",
    "cpt = 0\n",
    "\n",
    "for expression in os.listdir(base_path + \"train/\"):\n",
    "    for i in range(1,6):\n",
    "        cpt = cpt + 1\n",
    "        plt.subplot(7,5,cpt)\n",
    "        img = load_img(base_path + \"train/\" + expression + \"/\" +os.listdir(base_path + \"train/\" + expression)[i], target_size=(pic_size, pic_size))\n",
    "        plt.imshow(img, cmap=\"gray\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bcedb42d1126f4692f3aa09434eb35e5f7d1b3d6"
   },
   "source": [
    "Can you guess which images are related to which expressions? \n",
    "\n",
    "This task is quite easy for a human, but it may be a bit challenging for a predictive algorithm because:\n",
    "- the images have a low resolution\n",
    "- the faces are not in the same position\n",
    "- some images have text written on them\n",
    "- some people hide part of their faces with their hands\n",
    "\n",
    "However all this diversity of images will contribute to make a more generalizable model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "2fc206564d516020e9c3001c8a1448766aace848"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4103 fear images\n",
      "436 disgust images\n",
      "4982 neutral images\n",
      "7164 happy images\n",
      "3993 angry images\n",
      "3205 surprise images\n",
      "4938 sad images\n"
     ]
    }
   ],
   "source": [
    "# count number of train images for each expression\n",
    "\n",
    "for expression in os.listdir(base_path + \"train\"):\n",
    "    print(str(len(os.listdir(base_path + \"train/\" + expression))) + \" \" + expression + \" images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a685f736404cffdf2076bd9b941b8b170c3fa23d"
   },
   "source": [
    "The face expressions in our training dataset are pretty balanced, except for the 'disgust' category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "74f292f501e5a60adb5463f412f42e5584f3a6a5"
   },
   "source": [
    "# Setup the data generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "0041128a27f4da936ad63e90959797391736fc8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 28821 images belonging to 7 classes.\n",
      "Found 7066 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# number of images to feed into the NN for every batch\n",
    "batch_size = 128\n",
    "\n",
    "datagen_train = ImageDataGenerator()\n",
    "datagen_validation = ImageDataGenerator()\n",
    "\n",
    "train_generator = datagen_train.flow_from_directory(base_path + \"train\",\n",
    "                                                    target_size=(pic_size,pic_size),\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    class_mode='categorical',\n",
    "                                                    shuffle=True)\n",
    "\n",
    "validation_generator = datagen_validation.flow_from_directory(base_path + \"validation\",\n",
    "                                                    target_size=(pic_size,pic_size),\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    class_mode='categorical',\n",
    "                                                    shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "07b92d541185f6225ba74465e51bdf84bd74f547"
   },
   "source": [
    "Deep learning models are trained by being fed with batches of data. Keras has a very useful class to automatically feed data from a directory: ImageDataGenerator. \n",
    "\n",
    "It can also perform data augmentation while getting the images (randomly rotating the image, zooming, etc.). This method is often used as a way to artificially get more data when the dataset has a small size.\n",
    "\n",
    "The function flow_from_directory() specifies how the generator should import the images (path, image size, colors, etc.).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "eb856d203459e65c906749c8993aaaec7ed2faff"
   },
   "source": [
    "# Setup our Convolutional Neural Network (CNN)\n",
    "\n",
    "We chose to use a Convolutional Neural Network in order to tackle this face recognition problem. Indeed this type of Neural Network (NN) is good for extracting the features of images and is widely used for image analysis subjects like image classification.\n",
    "\n",
    "**Quick reminder of what a NN is:**\n",
    "\n",
    "A Neural Network is a learning framework that consists in multiple layers of artificial neurons (nodes). Each node gets weighted input data, passes it into an activation function and outputs the result of the function:\n",
    "\n",
    "![](https://skymind.ai/images/wiki/perceptron_node.png)\n",
    "\n",
    "A NN is composed of several layers of nodes:\n",
    "\n",
    "![](https://www.researchgate.net/profile/Martin_Musiol/publication/308414212/figure/fig1/AS:409040078295040@1474534162122/A-general-model-of-a-deep-neural-network-It-consists-of-an-input-layer-some-here-two.png)\n",
    "\n",
    "- An input layer that will get the data. The size of the input layer depends on the size of the input data.\n",
    "- Some hidden layers that will allow the NN to learn complex interactions within the data. A Neural Network with a lot of hidden layers is called a Deep Neural Network.\n",
    "- An output layer that will give the final result, for instance a class prediction. The size of this layer depends on the type of output we want to produce (e.g. how many classes do we want to predict?)\n",
    "\n",
    "Classic NNs are usually composed of several fully connected layers. This means that every neuron of one layer is connected to every neurons of the next layer. \n",
    "\n",
    "Convolutional Neural Networks also have Convolutional layers that apply sliding functions to group of pixels that are next to each other. Therefore those structures have a better understanding of patterns that we can observe in images. We will explain this in more details after.\n",
    "\n",
    "Now let's define the architecture of our CNN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_uuid": "4914afe556e0db3c03614ed1ac2138fd68d03815"
   },
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Input, Dropout, GlobalAveragePooling2D, Flatten, Conv2D, BatchNormalization, Activation, MaxPooling2D\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# number of possible label values\n",
    "nb_classes = 7\n",
    "\n",
    "# Initialising the CNN\n",
    "model = Sequential()\n",
    "\n",
    "# 1 - Convolution\n",
    "model.add(Conv2D(64,(3,3), padding='same', input_shape=(48, 48,1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 2nd Convolution layer\n",
    "model.add(Conv2D(128,(5,5), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 3rd Convolution layer\n",
    "model.add(Conv2D(512,(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 4th Convolution layer\n",
    "model.add(Conv2D(512,(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# Flattening\n",
    "model.add(Flatten())\n",
    "\n",
    "# Fully connected layer 1st layer\n",
    "model.add(Dense(256))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# Fully connected layer 2nd layer\n",
    "model.add(Dense(512))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(nb_classes, activation='softmax'))\n",
    "\n",
    "opt = Adam(lr=0.0001)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a59f0528c8c3226cfb82d9564fbc27561e6d78ed"
   },
   "source": [
    "We define our CNN with the following global architecture:\n",
    "- 4 convolutional layers\n",
    "- 2 fully connected layers\n",
    "\n",
    "The convolutional layers will extract relevant features from the images and the fully connected layers will focus on using these features to classify well our images. This architecture was inspired by the following work on the subject: https://github.com/jrishabh96/Facial-Expression-Recognition\n",
    "\n",
    "Let's focus on how our convolution layers work. Each of them contain the following operations:\n",
    "- A convolution operator: extracts features from the input image using sliding matrices to preserve the spatial relations between the pixels. The following image summarizes how it works:\n",
    "![](http://deeplearning.stanford.edu/wiki/images/6/6c/Convolution_schematic.gif)\n",
    "\n",
    "The green matrix corresponds to the raw image values. The orange sliding matrix is called a 'filter' or 'kernel'. This filter slides over the image by one pixel at each step (stride). During each step, we multiply the filter with the corresponding elements of the base matrix. There are different types of filters and each one will be able to retrieve different image features:\n",
    "![](https://ujwlkarn.files.wordpress.com/2016/08/screen-shot-2016-08-05-at-11-03-00-pm.png?w=342&h=562)\n",
    "\n",
    "- We apply the ReLU function to introduce non linearity in our CNN. Other functions like tanh or sigmoid could also be used, but ReLU has been found to perform better in most situations.\n",
    "- Pooling is used to reduce the dimensionality of each features while retaining the most important information. Like for the convolutional step, we apply a sliding function on our data. Different functions can be applied: max, sum, mean... The max function usually performs better.\n",
    "![](http://cs231n.github.io/assets/cnn/maxpool.jpeg)\n",
    "\n",
    "We also use some common techniques for each layer:\n",
    "- Batch normalization: improves the performance and stability of NNs by providing inputs with zero mean and unit variance.\n",
    "- Dropout: reduces overfitting by randomly not updating the weights of some nodes. This helps prevent the NN from relying on one node in the layer too much.\n",
    "\n",
    "We chose softmax as our last activation function as it is commonly used for multi-label classification.\n",
    "\n",
    "Now that our CNN is defined, we can compile it with a few more parameters. We chose the Adam optimizer as it is one of the most computationally effective. We chose the categorical cross-entropy as our loss function as it is quite relevant for classification tasks. Our metric will be the accuracy, which is also quite informative for classification tasks on balanced datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "883ca4ab4d9c00ac08434916e7713c8f9b4353ba"
   },
   "source": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e5d128b83c8d2182f6112287776ce7f37c63936d"
   },
   "source": [
    "Everything is set up, let's train our model now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "8d3db222ae6b74ab55df0d840d372b4306af6db0",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "225/225 [==============================] - 36s 161ms/step - loss: 2.0174 - acc: 0.2333 - val_loss: 1.7391 - val_acc: 0.2966\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.29659, saving model to model_weights.h5\n",
      "Epoch 2/50\n",
      "225/225 [==============================] - 31s 138ms/step - loss: 1.8401 - acc: 0.2873 - val_loss: 1.7091 - val_acc: 0.3311\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.29659 to 0.33108, saving model to model_weights.h5\n",
      "Epoch 3/50\n",
      "225/225 [==============================] - 31s 138ms/step - loss: 1.7354 - acc: 0.3318 - val_loss: 1.6665 - val_acc: 0.3711\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.33108 to 0.37114, saving model to model_weights.h5\n",
      "Epoch 4/50\n",
      "225/225 [==============================] - 32s 142ms/step - loss: 1.6591 - acc: 0.3643 - val_loss: 1.6144 - val_acc: 0.3890\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.37114 to 0.38902, saving model to model_weights.h5\n",
      "Epoch 5/50\n",
      "225/225 [==============================] - 31s 138ms/step - loss: 1.5971 - acc: 0.3886 - val_loss: 1.5129 - val_acc: 0.4226\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.38902 to 0.42260, saving model to model_weights.h5\n",
      "Epoch 6/50\n",
      "225/225 [==============================] - 31s 136ms/step - loss: 1.5372 - acc: 0.4093 - val_loss: 1.4734 - val_acc: 0.4344\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.42260 to 0.43442, saving model to model_weights.h5\n",
      "Epoch 7/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.4836 - acc: 0.4330 - val_loss: 1.4811 - val_acc: 0.4318\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.43442\n",
      "Epoch 8/50\n",
      "225/225 [==============================] - 31s 136ms/step - loss: 1.4470 - acc: 0.4421 - val_loss: 1.4688 - val_acc: 0.4421\n",
      "\n",
      "Epoch 00008: val_acc improved from 0.43442 to 0.44206, saving model to model_weights.h5\n",
      "Epoch 9/50\n",
      "225/225 [==============================] - 31s 136ms/step - loss: 1.3993 - acc: 0.4620 - val_loss: 1.3453 - val_acc: 0.4847\n",
      "\n",
      "Epoch 00009: val_acc improved from 0.44206 to 0.48472, saving model to model_weights.h5\n",
      "Epoch 10/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.3667 - acc: 0.4760 - val_loss: 1.3301 - val_acc: 0.4963\n",
      "\n",
      "Epoch 00010: val_acc improved from 0.48472 to 0.49625, saving model to model_weights.h5\n",
      "Epoch 11/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.3313 - acc: 0.4853 - val_loss: 1.2498 - val_acc: 0.5215\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.49625 to 0.52148, saving model to model_weights.h5\n",
      "Epoch 12/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.2999 - acc: 0.5017 - val_loss: 1.2690 - val_acc: 0.5228\n",
      "\n",
      "Epoch 00012: val_acc improved from 0.52148 to 0.52277, saving model to model_weights.h5\n",
      "Epoch 13/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.2721 - acc: 0.5160 - val_loss: 1.2040 - val_acc: 0.5381\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.52277 to 0.53805, saving model to model_weights.h5\n",
      "Epoch 14/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 1.2481 - acc: 0.5216 - val_loss: 1.2038 - val_acc: 0.5438\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.53805 to 0.54382, saving model to model_weights.h5\n",
      "Epoch 15/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 1.2171 - acc: 0.5372 - val_loss: 1.2361 - val_acc: 0.5284\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.54382\n",
      "Epoch 16/50\n",
      "225/225 [==============================] - 31s 136ms/step - loss: 1.1997 - acc: 0.5451 - val_loss: 1.1829 - val_acc: 0.5483\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.54382 to 0.54828, saving model to model_weights.h5\n",
      "Epoch 17/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.1770 - acc: 0.5482 - val_loss: 1.2091 - val_acc: 0.5362\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.54828\n",
      "Epoch 18/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 1.1561 - acc: 0.5598 - val_loss: 1.1495 - val_acc: 0.5712\n",
      "\n",
      "Epoch 00018: val_acc improved from 0.54828 to 0.57120, saving model to model_weights.h5\n",
      "Epoch 19/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 1.1332 - acc: 0.5692 - val_loss: 1.1241 - val_acc: 0.5749\n",
      "\n",
      "Epoch 00019: val_acc improved from 0.57120 to 0.57495, saving model to model_weights.h5\n",
      "Epoch 20/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 1.1245 - acc: 0.5720 - val_loss: 1.1069 - val_acc: 0.5899\n",
      "\n",
      "Epoch 00020: val_acc improved from 0.57495 to 0.58994, saving model to model_weights.h5\n",
      "Epoch 21/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 1.1041 - acc: 0.5799 - val_loss: 1.1192 - val_acc: 0.5729\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.58994\n",
      "Epoch 22/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 1.0798 - acc: 0.5880 - val_loss: 1.1452 - val_acc: 0.5761\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.58994\n",
      "Epoch 23/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 1.0635 - acc: 0.6001 - val_loss: 1.0998 - val_acc: 0.5953\n",
      "\n",
      "Epoch 00023: val_acc improved from 0.58994 to 0.59527, saving model to model_weights.h5\n",
      "Epoch 24/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 1.0499 - acc: 0.6025 - val_loss: 1.0977 - val_acc: 0.5918\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.59527\n",
      "Epoch 25/50\n",
      "225/225 [==============================] - 31s 137ms/step - loss: 1.0348 - acc: 0.6067 - val_loss: 1.1280 - val_acc: 0.5904\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.59527\n",
      "Epoch 26/50\n",
      "225/225 [==============================] - 31s 136ms/step - loss: 1.0202 - acc: 0.6145 - val_loss: 1.0765 - val_acc: 0.6007\n",
      "\n",
      "Epoch 00026: val_acc improved from 0.59527 to 0.60075, saving model to model_weights.h5\n",
      "Epoch 27/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 1.0023 - acc: 0.6207 - val_loss: 1.0696 - val_acc: 0.6016\n",
      "\n",
      "Epoch 00027: val_acc improved from 0.60075 to 0.60161, saving model to model_weights.h5\n",
      "Epoch 28/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.9899 - acc: 0.6258 - val_loss: 1.0555 - val_acc: 0.6082\n",
      "\n",
      "Epoch 00028: val_acc improved from 0.60161 to 0.60824, saving model to model_weights.h5\n",
      "Epoch 29/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.9636 - acc: 0.6392 - val_loss: 1.0827 - val_acc: 0.5951\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.60824\n",
      "Epoch 30/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.9683 - acc: 0.6348 - val_loss: 1.0603 - val_acc: 0.6116\n",
      "\n",
      "Epoch 00030: val_acc improved from 0.60824 to 0.61156, saving model to model_weights.h5\n",
      "Epoch 31/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.9433 - acc: 0.6426 - val_loss: 1.1015 - val_acc: 0.5925\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.61156\n",
      "Epoch 32/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.9205 - acc: 0.6543 - val_loss: 1.0848 - val_acc: 0.6033\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.61156\n",
      "Epoch 33/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.9164 - acc: 0.6540 - val_loss: 1.0945 - val_acc: 0.6054\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.61156\n",
      "Epoch 34/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.8944 - acc: 0.6613 - val_loss: 1.0408 - val_acc: 0.6231\n",
      "\n",
      "Epoch 00034: val_acc improved from 0.61156 to 0.62309, saving model to model_weights.h5\n",
      "Epoch 35/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.8857 - acc: 0.6651 - val_loss: 1.0440 - val_acc: 0.6231\n",
      "\n",
      "Epoch 00035: val_acc improved from 0.62309 to 0.62309, saving model to model_weights.h5\n",
      "Epoch 36/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 0.8667 - acc: 0.6760 - val_loss: 1.0408 - val_acc: 0.6198\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.62309\n",
      "Epoch 37/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.8513 - acc: 0.6819 - val_loss: 1.0553 - val_acc: 0.6280\n",
      "\n",
      "Epoch 00037: val_acc improved from 0.62309 to 0.62799, saving model to model_weights.h5\n",
      "Epoch 38/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.8282 - acc: 0.6882 - val_loss: 1.0357 - val_acc: 0.6268\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.62799\n",
      "Epoch 39/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.8224 - acc: 0.6916 - val_loss: 1.0770 - val_acc: 0.6180\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.62799\n",
      "Epoch 40/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225 [==============================] - 30s 133ms/step - loss: 0.8138 - acc: 0.6960 - val_loss: 1.0631 - val_acc: 0.6248\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.62799\n",
      "Epoch 41/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.7960 - acc: 0.7033 - val_loss: 1.0399 - val_acc: 0.6329\n",
      "\n",
      "Epoch 00041: val_acc improved from 0.62799 to 0.63289, saving model to model_weights.h5\n",
      "Epoch 42/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.7713 - acc: 0.7117 - val_loss: 1.0080 - val_acc: 0.6473\n",
      "\n",
      "Epoch 00042: val_acc improved from 0.63289 to 0.64730, saving model to model_weights.h5\n",
      "Epoch 43/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 0.7598 - acc: 0.7161 - val_loss: 1.0069 - val_acc: 0.6467\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.64730\n",
      "Epoch 44/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.7463 - acc: 0.7196 - val_loss: 1.0760 - val_acc: 0.6300\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.64730\n",
      "Epoch 45/50\n",
      "225/225 [==============================] - 31s 136ms/step - loss: 0.7406 - acc: 0.7236 - val_loss: 1.0751 - val_acc: 0.6276\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.64730\n",
      "Epoch 46/50\n",
      "225/225 [==============================] - 30s 135ms/step - loss: 0.7208 - acc: 0.7317 - val_loss: 1.0152 - val_acc: 0.6506\n",
      "\n",
      "Epoch 00046: val_acc improved from 0.64730 to 0.65062, saving model to model_weights.h5\n",
      "Epoch 47/50\n",
      "225/225 [==============================] - 30s 134ms/step - loss: 0.6982 - acc: 0.7412 - val_loss: 1.0375 - val_acc: 0.6522\n",
      "\n",
      "Epoch 00047: val_acc improved from 0.65062 to 0.65221, saving model to model_weights.h5\n",
      "Epoch 48/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.6907 - acc: 0.7437 - val_loss: 1.0152 - val_acc: 0.6490\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.65221\n",
      "Epoch 49/50\n",
      "225/225 [==============================] - 30s 133ms/step - loss: 0.6879 - acc: 0.7431 - val_loss: 1.0627 - val_acc: 0.6398\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.65221\n",
      "Epoch 50/50\n",
      "225/225 [==============================] - 30s 132ms/step - loss: 0.6723 - acc: 0.7499 - val_loss: 1.1159 - val_acc: 0.6384\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.65221\n",
      "CPU times: user 26min 13s, sys: 6min 22s, total: 32min 35s\n",
      "Wall time: 25min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# number of epochs to train the NN\n",
    "epochs = 50\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "checkpoint = ModelCheckpoint(\"model_weights.h5\", monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "callbacks_list = [checkpoint]\n",
    "\n",
    "history = model.fit_generator(generator=train_generator,\n",
    "                                steps_per_epoch=train_generator.n//train_generator.batch_size,\n",
    "                                epochs=epochs,\n",
    "                                validation_data = validation_generator,\n",
    "                                validation_steps = validation_generator.n//validation_generator.batch_size,\n",
    "                                callbacks=callbacks_list\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8d36c74bc52eb86d9b398585b28c7450639e90b3"
   },
   "source": [
    "Our best model managed to obtain a validation accuracy of approximately 65%, which is quite good given the fact that our target class has 7 possible values!\n",
    "\n",
    "At each epoch, Keras checks if our model performed better than during the previous epochs. If it is the case, the new best model weights are saved into a file. This will allow us to load directly the weights of our model without having to re-train it if we want to use it.\n",
    "\n",
    "We also have to save the structure of our CNN (layers etc.) into a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_uuid": "2609d0c01ce8435092c687a326fe7b432917868a"
   },
   "outputs": [],
   "source": [
    "# serialize model structure to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3b1544cf2a38f9967dd357ed94ad4a59b9a86191"
   },
   "source": [
    "# Analyze the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "571a5de1f9ca4a5648de2c085a67afce0e784531"
   },
   "source": [
    "We got outputs at each step of the training phase. All those outputs were saved into the 'history' variable. We can use it to plot the evolution of the loss and accuracy on both the train and validation datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "afa554be42c812c41438a89126e26eb459c3dd74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the evolution of Loss and Acuracy on the train and validation sets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.suptitle('Optimizer : Adam', fontsize=10)\n",
    "plt.ylabel('Loss', fontsize=16)\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.ylabel('Accuracy', fontsize=16)\n",
    "plt.plot(history.history['acc'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_acc'], label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "25e7825e2fd5cbff9cac17a577ff2925d476584d"
   },
   "source": [
    "The validation accuracy starts to stabilize at the end of the 50 epochs between 60% and 65% accuracy.\n",
    "\n",
    "The training loss is slightly higher than the validation loss for the first epochs which can be surprising. Indeed we are more used to see higher validation losses than training losses in machine learning. Here this is simply due to the presence of dropout, which is only applied during the training phase and not during the validation phase.\n",
    "\n",
    "We can see that the training loss is becoming much smaller than the validation loss after the 20th iteration. This means that our model starts to overfit our training dataset after too much epochs. That is why the validation loss does not decrease a lot after. One solution consists in early-stopping the training of the model. \n",
    "\n",
    "We could also use some different dropout values and performing data augmentation. Those methods were tested on this dataset, but they did not significantly increase the validation accuracy although they reduced the overfitting effect. Using them slightly increased the training duration of the model.\n",
    "\n",
    "Finally we can plot the confusion matrix in order to see how our model classified the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "4d2a670697c28add1f73f5d41c129c2df974f7b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the confusion matrix of our predictions\n",
    "\n",
    "# compute predictions\n",
    "predictions = model.predict_generator(generator=validation_generator)\n",
    "y_pred = [np.argmax(probas) for probas in predictions]\n",
    "y_test = validation_generator.classes\n",
    "class_names = validation_generator.class_indices.keys()\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "# compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names, title='Normalized confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f6f66768b63e000a9dcdcbd64d30d5f66ef55a37"
   },
   "source": [
    "Our model is very good for predicting happy and surprised faces. However it predicts quite poorly feared faces because it confuses them with sad faces.\n",
    "\n",
    "With more research and more resources this model could certainly be improved, but the goal of this study was primarily to focus on obtaining a fairly good model compared to what has been done in this field. \n",
    "\n",
    "Now it's time to try our model in a real situation! We will use flask to serve our model in order to perform real-time predictions with a webcam input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "6076d860425d9e1bf4e837d1d618823436739e37"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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